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Conceptualising influencer marketing

Social media has become a prolific tool for companies to build their brands. An effective way to interact with stakeholders on social media has been the relatively new discipline of ‘influencer marketing’. Here, companies engage social media stars to use their large fan-base to promote products and services on their brand’s behalf. While related to the promotional tactic of word-of-mouth marketing, influencer marketing lacks a theoretical foundation in the academic discourse. This paper aims to fill this gap by offering a conceptualisation to operationalize the new discipline in practice. The conceptualisation proposes brand owners a methodology to choose the right influencers for their brands and guides influencers to perform optimally with their fan base. Lastly, a consumer perspective is taken to the discussion to emphasize the relevance of influencer marketing in the consumer purchase decision-making process.

Waarom zijn mensen actief op online communities?

Het succes van een merk wordt steeds meer afhankelijk van de manier waarop sociale media worden ingezet. Organisaties proberen met behulp van sociale media brand communities te ontwikkelen om informatie te delen, merkwaarden te communiceren en klantrelaties te onderhouden

Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web

Retail industry consists of the establishment of selling consumer goods (i.e. technology, pharmaceuticals, food and beverages, apparels and accessories, home improvement etc.) and services (i.e. specialty and movies) to customers through multiple channels of distribution including both the traditional brickand-mortar and online retailing. Managing corporate reputation of retail companies is crucial as it has many advantages, for instance, it has been proven to impact generated revenues (Wang et al., 2016). But, in order to be able to manage corporate reputation, one has to be able to measure it, or, nowadays even better, listen to relevant social signals that are out there on the public web. One of the most extensive and widely used frameworks for measuring corporate reputation is through conducting elaborated surveys with respective stakeholders (Fombrun et al., 2015). This approach is valuable but deemed to be laborious and resource-heavy and will not allow to generate automatic alerts and quick and live insights that are extremely needed in this era of internet. For these purposes a social listening approach is needed that can be tailored to online data such as consumer reviews as the main data source. Online review datasets are a form of electronic Word-of-Mouth (WOM) that, when a data source is picked that is relevant to retail, commonly contain relevant information about customers’ perceptions regarding products (Pookulangara, 2011) and that are massively available. The algorithm that we have built in our application provides retailers with reputation scores for all variables that are deemed to be relevant to retail in the model of Fombrun et al. (2015). Examples of such variables for products and services are high quality, good value, stands behind, and meets customer needs. We propose a new set of subvariables with which these variables can be operationalized for retail in particular. Scores are being calculated using proportions of positive opinion pairs such as <fast, delivery> or <rude, staff> that have been designed per variable. With these important insights extracted, companies can act accordingly and proceed to improve their corporate reputation. It is important to emphasize that, once the design is complete and implemented, all processing can be performed completely automatic and unsupervised. The application makes use of a state of the art aspect-based sentiment analysis (ABSA) framework because of ABSA’s ability to generate sentiment scores for all relevant variables and aspects. Since most online data is in open form and we deliberately want to avoid labelling any data by human experts, the unsupervised aspectator algorithm has been picked. It employs a lexicon to calculate sentiment scores and uses syntactic dependency paths to discover candidate aspects (Bancken et al., 2014). We have applied our approach to a large number of online review datasets that we sampled from a list of 50 top global retailers according to National Retail Federation (2020), including both offline and online operation, and that we scraped from trustpilot, a public website that is well-known to retailers. The algorithm has carefully been evaluated by manually annotating a randomly sampled subset of the datasets for validation purposes by two independent annotators. The Kappa’s score on this subset was 80%.

Dragen social media activiteiten bij aan de reputatie van bedrijven? (online article)

Web based higher education, the inclusion/exclusion paradox

Increasingly, education is delivered through computers and the internet. This article highlights that while such development is beneficial for some students with functional impairments, it might be excluding others if insufficient attention is paid to accessibility. Both the electronic learning environment (Blackboard, WebCT and the like) as well as the content author need to design for accessibility.

Burgerparticipatie verhogen in 140 tekens?

Een online onderzoek naar de effecten op online participatiegedrag van verschillende communicatiestrategieën en het gebruik van hashtags.